Capacity planning for platform services: Agent availability, compensation, and dual sourcing

One of the key decisions for an on-demand service platform is to plan capacity to meet uncertain demand. This problem is also compounded by the operating environment and multiple stakeholder perspectives. For example, capacity is typically determined not only by multiple supply sources but also by the platform’s compensation scheme, as this affects labor pool availability. In addition, since on-demand platforms do not service demand using permanent (e.g., full-time) employees, it is likely that the employee pool is heterogeneous in their income preferences. In this paper, we analytically characterize the capacity planning problem for an e-hailing platform offering transportation service to customers (such as Uber and Lyft) using independent agents (or drivers). In the presence of uncertain demand, the unique features incorporated into our analysis include sources of supply (single/dual), driver absenteeism rates, platform compensation schemes, labor pool constraints, and heterogeneity in drivers’ income-earning orientation. Interestingly, one of our major findings is that labor pool constraints determine the types of drivers that the platform should recruit. In the absence of such constraints, the platform should use only “unreliable” drivers, whereas both reliable and unreliable drivers should be employed when the labor pool is constrained. From a platform perspective, a lower compensation fraction should be offered under a post-paid scheme than under a pre-paid compensation scheme. The model and its results are validated using empirical data from different markets. A sensitivity analysis is performed to assess the robustness of this approach across various demand, payment, and driver-type scenarios.